5,174 research outputs found
Transformation-Invariant Dictionary Learning for Classification with 1-Sparse Representations
Sparse representations of images in well-designed dictionaries can be used for effective classification. Meanwhile, training data available in most realistic settings are likely to be exposed to geometric transformations, which poses a challenge for the design of good dictionaries. In this work, we study the problem of learning class-representative dictionaries from geometrically transformed image sets. In order to efficiently take account of arbitrary geometric transformations in the learning, we adopt a representation of the dictionaries in an analytic basis. Then, the proposed algorithm learns atoms that are attracted to the samples of their own class while being repelled from the samples of other classes so that the discrimination between different classes is promoted. The dictionary learning objective is formulated such that it enhances the class-discrimination capabilities of individual atoms rather than the ones of the subspaces they generate, which renders the designed dictionaries especially suitable for fast classification of query images with very sparse approximations. Experimental results demonstrate the performance of the proposed method in handwritten digit recognition applications
Unsupervised Feature Learning by Deep Sparse Coding
In this paper, we propose a new unsupervised feature learning framework,
namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer
architecture for visual object recognition tasks. The main innovation of the
framework is that it connects the sparse-encoders from different layers by a
sparse-to-dense module. The sparse-to-dense module is a composition of a local
spatial pooling step and a low-dimensional embedding process, which takes
advantage of the spatial smoothness information in the image. As a result, the
new method is able to learn several levels of sparse representation of the
image which capture features at a variety of abstraction levels and
simultaneously preserve the spatial smoothness between the neighboring image
patches. Combining the feature representations from multiple layers, DeepSC
achieves the state-of-the-art performance on multiple object recognition tasks.Comment: 9 pages, submitted to ICL
A Deep Representation for Invariance And Music Classification
Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.Comment: 5 pages, CBMM Memo No. 002, (to appear) IEEE 2014 International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014
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